Cadiovascular Diseases (CVD)
Cardiovascular diseases (CVD) include, for example, coronary heart disease (CHD) and hypertension. CHD includes, for example, myocardial infarction (MI).
Coronary Heart Disease (CHD), Including Myocardial Infarction (MI)
The present invention relates to SNPs that are associated with the occurrence of coronary heart disease (CHD), particularly myocardial infarction (MI).
CHD is defined in the Framingham Heart Study as encompassing MI, angina pectoris, coronary insufficiency (which is manifested as ischemia, that is, impaired oxygen flow to the heart muscle), and coronary heart disease death (Wilson et al., Circulation 97:1837-1847 (1998)). CHD may be recorded through clinical records that indicate the following interventions: coronary artery bypass graft (CABG), angioplasty (e.g., percutaneous transluminal coronary angioplasty (PTCA)), and revascularization (stent placement), in addition to clinical records of MI, angina, or coronary death.
As used herein, CHD is defined in accordance with how this term is defined in the Framingham Heart Study (i.e., as encompassing MI, angina pectoris, coronary insufficiency, and coronary heart disease death). Angina pectoris includes unstable angina in particular.
The SNPs described herein may further be useful for such cardiovascular events as vulnerable plaque and stroke.
Myocardial Infarction (MI)
Myocardial infarction (MI), also referred to as a “heart attack”, is the most common cause of mortality in developed countries. The incidence of MI is still high despite currently available preventive measures and therapeutic intervention. More than 1,500,000 people in the U.S. suffer acute MI each year, many without seeking help due to unrecognized MI, and one third of these people die. The lifetime risk of coronary artery disease events at age 40 is 42.4% for men, nearly one in two, and 24.9% for women, or one in four. D. M. Lloyd-Jones, Lancet 353:89-92 (1999).
MI is a multifactorial disease that involves atherogenesis, thrombus formation and propagation. Thrombosis can result in complete or partial occlusion of coronary arteries. The luminal narrowing or blockage of coronary arteries reduces oxygen and nutrient supply to the cardiac muscle (cardiac ischemia), leading to myocardial necrosis and/or stunning. MI, unstable angina, and sudden ischemic death are clinical manifestations of cardiac muscle damage. All three endpoints are part of acute coronary syndrome since the underlying mechanisms of acute complications of atherosclerosis are considered to be the same.
Atherogenesis, the first step of pathogenesis of MI, is a complex interaction between blood elements, mechanical forces, disturbed blood flow, and vessel wall abnormality that results in plaque accumulation. An unstable (vulnerable) plaque was recognized as an underlying cause of arterial thrombotic events and MI. A vulnerable plaque is a plaque, often not stenotic, that has a high likelihood of becoming disrupted or eroded, thus forming a thrombogenic focus. MI due to a vulnerable plaque is a complex phenomenon that includes: plaque vulnerability, blood vulnerability (hypercoagulation, hypothrombolysis), and heart vulnerability (sensitivity of the heart to ischemia or propensity for arrhythmia). Recurrent myocardial infarction (RMI) can generally be viewed as a severe form of MI progression caused by multiple vulnerable plaques that are able to undergo pre-rupture or a pre-erosive state, coupled with extreme blood coagulability.
The current diagnosis of MI is based on the levels of troponin I or T that indicate the cardiac muscle progressive necrosis, impaired electrocardiogram (ECG), and detection of abnormal ventricular wall motion or angiographic data (the presence of acute thrombi). However, due to the asymptomatic nature of 25% of acute MIs (absence of atypical chest pain, low ECG sensitivity), a significant portion of MIs are not diagnosed and therefore not treated appropriately (e.g., prevention of recurrent MIs).
MI risk assessment and prognosis is currently done using classic risk factors or the recently introduced Framingham Risk Index. Both of these assessments put a significant weight on LDL levels to justify preventive treatment. However, it is well established that half of all MIs occur in individuals without overt hyperlipidemia.
Other emerging risk factors of MI are inflammatory biomarkers such as C-reactive protein (CRP), ICAM-1, SAA, TNF α, homocysteine, impaired fasting glucose, new lipid markers (ox LDL, Lp-a, MAD-LDL, etc.) and pro-thrombotic factors (fibrinogen, PAI-1). These markers have significant limitations such as low specificity and low positive predictive value, and the need for multiple reference intervals to be used for different groups of people (e.g., males-females, smokers-non smokers, hormone replacement therapy users, different age groups). These limitations diminish the utility of such markers as independent prognostic markers for MI screening.
Genetics plays an important role in MI risk. Families with a positive family history of MI account for 14% of the general population, 72% of premature MIs, and 48% of all MIs. R. R. Williams, Am J Cardiology 87:129 (2001). In addition, replicated linkage studies have revealed evidence of multiple regions of the genome that are associated with MI and relevant to MI genetic traits, including regions on chromosomes 14, 2, 3 and 7, implying that genetic risk factors influence the onset, manifestation, and progression of MI. U. Broeckel, Nature Genetics 30:210 (2002); S. Harrap, Arterioscler Thromb Vasc Biol 22:874-878 (2002); A. Shearman, Human Molecular Genetics 9:1315-1320 (2000). Recent association studies have identified allelic variants that are associated with acute complications of CHD, including allelic variants of the ApoE, ApoA5, Lpa, APOCIII, and Klotho genes.
Genetic markers such as single nucleotide polymorphisms (SNPs) are preferable to other types of biomarkers. Genetic markers that are prognostic for MI can be genotyped early in life and could predict individual response to various risk factors. The combination of serum protein levels and genetic predisposition revealed by genetic analysis of susceptibility genes can provide an integrated assessment of the interaction between genotypes and environmental factors, resulting in synergistically increased prognostic value of diagnostic tests.
Thus, there is an urgent need for novel genetic markers that are predictive of predisposition to CHD such as MI, particularly for individuals who are unrecognized as having a predisposition to MI. Such genetic markers may enable prognosis of MI in much larger populations compared with the populations that can currently be evaluated by using existing risk factors and biomarkers. The availability of a genetic test may allow, for example, appropriate preventive treatments for acute coronary events to be provided for susceptible individuals (such preventive treatments may include, for example, statin treatments and statin dose escalation, as well as changes to modifiable risk factors), lowering of the thresholds for ECG and angiography testing, and allow adequate monitoring of informative biomarkers. Moreover, the discovery of genetic markers associated with MI will provide novel targets for therapeutic intervention or preventive treatments of MI, and enable the development of new therapeutic agents for treating or preventing MI and other cardiovascular disorders.
Furthermore, novel genetic markers that are predictive of predisposition to MI can be particularly useful for identifying individuals who are at risk for early-onset MI. “Early-onset MI” may be defined as MI in men who are less than 55 years of age and women who are less than 65 years of age. K. O. Akosah et al., “Preventing myocardial infarction in the young adult in the first place: How do the National Cholesterol Education Panel III guidelines perform?” JACC 41(9):1475-1479 (2003). Individuals who experience early-onset MI may not be effectively identified by current cholesterol treatment guidelines, such as those suggested by the National Cholesterol Education Program. In one study, for example, a significant number of individuals who suffered MI at an earlier age (≤50 years) were shown to have LDL cholesterol below 100 mg/dl. K. O. Akosah et al., “Myocardial infarction in young adults with low-density lipoprotein cholesterol levels less than or equal to 100 mg/dl. Clinical profile and 1-year outcomes.” Chest 120:1953-1958 (2001). Because risk for MI can be reduced by lifestyle changes and by treatment of modifiable risk factors, better methods to identify individuals at risk for early-onset MI could be useful for making preventive treatment decisions, especially considering that these patients may not be identified for medical management by conventional treatment guidelines. Genetic markers for risk of early-onset MI could potentially be incorporated into individual risk assessment protocols, as they have the advantage of being easily detected at any age.
Hypertension
Hypertension is a significant, modifiable risk factor for both CHD and stroke; two of the top three causes of mortality in the United States (Kearney et al., Lancet. 2005; 365:217-223; and Centers for Disease Control and Prevention, National Center for Health Statistics, FastStats). The prevalence of hypertension in US adults is estimated to be 29% (Ostchega et al., 2008, National Center for Health Statistics data brief no. 3), and the prevalence is expected to increase in the future (Kearney et al., Lancet. 2005; 365:217-223; and Hajjar et al., JAMA. 2003; 290:199-206). While about 5% of hypertension has known causes (classified as secondary hypertension), the majority of hypertension is due to unknown causes (classified as essential hypertension) (Cowley et al., Nature Reviews Genetics. 2006; 7:829-840). It is estimated that genetic variation contributes to between 30-60% of inter-individual blood pressure variation (Binder, Curr Opin Cardiol. 2007; 22:176-184) but the identity and nature of the contributing genetic loci are largely unknown.
Statin Treatment
HMG-CoA reductase inhibitors (statins) are used for the treatment and prevention of CVD, particularly MI. Reduction of MI and other coronary events and total mortality by treatment with HMG-CoA reductase inhibitors has been demonstrated in a number of randomized, double-blinded, placebo-controlled prospective trials. D. D. Waters, Clin Cardiol 24(8 Suppl):III3-7 (2001); B. K. Singh and J. L. Mehta, Curr Opin Cardiol 17(5):503-11 (2002). These drugs have their primary effect through the inhibition of hepatic cholesterol synthesis, thereby upregulating LDL receptors in the liver. The resultant increase in LDL catabolism results in decreased circulating LDL, a major risk factor for cardiovascular disease.
Statins can be divided into two types according to their physicochemical and pharmacokinetic properties. Statins such as lovastatin, simvastatin, atorvastatin, and cerevastatin are lipophilic in nature and, as such, diffuse across membranes and thus are highly cell permeable. Hydrophilic statins such as pravastatin are more polar, such that they require specific cell surface transporters for cellular uptake. K. Ziegler and W. Stunkel, Biochim Biophys Acta 1139(3):203-9 (1992); M. Yamazaki et al., Am J Physiol 264(1 Pt 1):G36-44 (1993); T. Komai et al., Biochem Pharmacol 43(4):667-70 (1992). The latter statin utilizes a transporter, OATP2, whose tissue distribution is confined to the liver and, therefore, they are relatively hepato-specific inhibitors. B. Hsiang et al., J Biol Chem 274(52):37161-37168 (1999). The former statins, not requiring specific transport mechanisms, are available to all cells and they can directly impact a much broader spectrum of cells and tissues. These differences in properties may influence the spectrum of activities that each statin possesses. Pravastatin, for instance, has a low myopathic potential in animal models and myocyte cultures compared to lipophilic statins. B. A. Masters et al., Toxicol Appl Pharmacol 131(1):163-174 (1995); K. Nakahara et al., Toxicol Appl Pharmacol 152(1):99-106 (1998); J. C. Reijneveld et al., Pediatr Res 39(6):1028-1035 (1996).
Evidence from gene association studies is accumulating to indicate that responses to drugs are, indeed, at least partly under genetic control. As such, pharmacogenetics—the study of variability in drug responses attributed to hereditary factors in different populations—may significantly assist in providing answers toward meeting this challenge. A. D. Roses, Nature 405(6788):857-865 (2000); V. Mooser et al., J Thromb Haemost 1(7):1398-1402 (2003); L. M. Humma and S. G. Terra, Am J Health Syst Pharm 59(13):1241-1252 (2002). Numerous associations have been reported between selected genotypes, as defined by SNPs and other genetic sequence variations, and specific responses to cardiovascular drugs. Polymorphisms in several genes have been suggested to influence responses to statins including CETP (J. A. Kuivenhoven et al., N Engl J Med 338(2):86-93 (1998)), beta-fibrinogen (M. P. de Maat et al., Arterioscler Thromb Vasc Biol 18(2):265-71 (1998)), hepatic lipase (A. Zambon et al., Circulation 103(6):792-798 (2001)), lipoprotein lipase (J. W. Jukema et al., Circulation 94(8):1913-1918 (1996)), glycoprotein IIIa (P. F. Bray et al., Am J Cardiol 88(4):347-352 (2001)), stromelysin-1 (M. P. de Maat et al., Am J Cardiol 83(6):852-856 (1999)), and apolipoprotein E (L. U. Gerdes et al., Circulation 101(12):1366-1371 (2000); J. Pedro-Botet et al., Atherosclerosis 158(1):183-193 (2001)). Some of these variants were shown to effect clinical events while others were associated with changes in surrogate endpoints.
Thus, there is a need for genetic markers that can be used to predict an individual's responsiveness to statins. For example, there is a growing need to better identify people who have the highest chance of benefiting from statins, and those who have the lowest risk of developing side-effects. For example, severe myopathies represent a significant risk for a low percentage of the patient population, and this may be a particular concern for patients who are treated more aggressively with statins.
Single Nucleotide Polymorphisms (SNPs)
The genomes of all organisms undergo spontaneous mutation in the course of their continuing evolution, generating variant forms of progenitor genetic sequences. Gusella, Ann Rev Biochem 55:831-854 (1986). A variant form may confer an evolutionary advantage or disadvantage relative to a progenitor form or may be neutral. In some instances, a variant form confers an evolutionary advantage to the species and is eventually incorporated into the DNA of many or most members of the species and effectively becomes the progenitor form. Additionally, the effects of a variant form may be both beneficial and detrimental, depending on the circumstances. For example, a heterozygous sickle cell mutation confers resistance to malaria, but a homozygous sickle cell mutation is usually lethal. In many cases, both progenitor and variant forms survive and co-exist in a species population. The coexistence of multiple forms of a genetic sequence gives rise to genetic polymorphisms, including SNPs.
Approximately 90% of all genetic polymorphisms in the human genome are SNPs. SNPs are single base positions in DNA at which different alleles, or alternative nucleotides, exist in a population. The SNP position (interchangeably referred to herein as SNP, SNP site, SNP locus, SNP marker, or marker) is usually preceded by and followed by highly conserved sequences of the allele (e.g., sequences that vary in less than 1/100 or 1/1000 members of the populations). An individual may be homozygous or heterozygous for an allele at each SNP position. A SNP can, in some instances, be referred to as a “cSNP” to denote that the nucleotide sequence containing the SNP is an amino acid coding sequence.
A SNP may arise from a substitution of one nucleotide for another at the polymorphic site. Substitutions can be transitions or transversions. A transition is the replacement of one purine nucleotide by another purine nucleotide, or one pyrimidine by another pyrimidine. A transversion is the replacement of a purine by a pyrimidine, or vice versa. A SNP may also be a single base insertion or deletion variant referred to as an “indel.” Weber et al., “Human diallelic insertion/deletion polymorphisms,” Am J Hum Genet 71(4):854-62 (October 2002).
A synonymous codon change, or silent mutation/SNP (terms such as “SNP,” “polymorphism,” “mutation,” “mutant,” “variation,” and “variant” are used herein interchangeably), is one that does not result in a change of amino acid due to the degeneracy of the genetic code. A substitution that changes a codon coding for one amino acid to a codon coding for a different amino acid (i.e., a non-synonymous codon change) is referred to as a missense mutation. A nonsense mutation results in a type of non-synonymous codon change in which a stop codon is formed, thereby leading to premature termination of a polypeptide chain and a truncated protein. A read-through mutation is another type of non-synonymous codon change that causes the destruction of a stop codon, thereby resulting in an extended polypeptide product. While SNPs can be bi-, tri-, or tetra-allelic, the vast majority of the SNPs are bi-allelic, and are thus often referred to as “bi-allelic markers,” or “di-allelic markers.”
As used herein, references to SNPs and SNP genotypes include individual SNPs and/or haplotypes, which are groups of SNPs that are generally inherited together. Haplotypes can have stronger correlations with diseases or other phenotypic effects compared with individual SNPs, and therefore may provide increased diagnostic accuracy in some cases. Stephens et al., Science 293:489-493 (July 2001).
Causative SNPs are those SNPs that produce alterations in gene expression or in the expression, structure, and/or function of a gene product, and therefore are most predictive of a possible clinical phenotype. One such class includes SNPs falling within regions of genes encoding a polypeptide product, i.e. cSNPs. These SNPs may result in an alteration of the amino acid sequence of the polypeptide product (i.e., non-synonymous codon changes) and give rise to the expression of a defective or other variant protein. Furthermore, in the case of nonsense mutations, a SNP may lead to premature termination of a polypeptide product. Such variant products can result in a pathological condition, e.g., genetic disease. Examples of genes in which a SNP within a coding sequence causes a genetic disease include sickle cell anemia and cystic fibrosis.
Causative SNPs do not necessarily have to occur in coding regions; causative SNPs can occur in, for example, any genetic region that can ultimately affect the expression, structure, and/or activity of the protein encoded by a nucleic acid. Such genetic regions include, for example, those involved in transcription, such as SNPs in transcription factor binding domains, SNPs in promoter regions, in areas involved in transcript processing, such as SNPs at intron-exon boundaries that may cause defective splicing, or SNPs in mRNA processing signal sequences such as polyadenylation signal regions. Some SNPs that are not causative SNPs nevertheless are in close association with, and therefore segregate with, a disease-causing sequence. In this situation, the presence of a SNP correlates with the presence of, or predisposition to, or an increased risk in developing the disease. These SNPs, although not causative, are nonetheless also useful for diagnostics, disease predisposition screening, and other uses.
An association study of a SNP and a specific disorder involves determining the presence or frequency of the SNP allele in biological samples from individuals with the disorder of interest, such as CVD, and comparing the information to that of controls (i.e., individuals who do not have the disorder; controls may be also referred to as “healthy” or “normal” individuals) who are preferably of similar age and race. The appropriate selection of patients and controls is important to the success of SNP association studies. Therefore, a pool of individuals with well-characterized phenotypes is extremely desirable.
A SNP may be screened in diseased tissue samples or any biological sample obtained from a diseased individual, and compared to control samples, and selected for its increased (or decreased) occurrence in a specific pathological condition, such as pathologies related to CVD and in particular, CHD (particularly MI) and hypertension. Once a statistically significant association is established between one or more SNP(s) and a pathological condition (or other phenotype) of interest, then the region around the SNP can optionally be thoroughly screened to identify the causative genetic locus/sequence(s) (e.g., causative SNP/mutation, gene, regulatory region, etc.) that influences the pathological condition or phenotype. Association studies may be conducted within the general population and are not limited to studies performed on related individuals in affected families (linkage studies).
Clinical trials have shown that patient response to treatment with pharmaceuticals is often heterogeneous. There is a continuing need to improve pharmaceutical agent design and therapy. In that regard, SNPs can be used to identify patients most suited to therapy with particular pharmaceutical agents (this is often termed “pharmacogenomics”). Similarly, SNPs can be used to exclude patients from certain treatment due to the patient's increased likelihood of developing toxic side effects or their likelihood of not responding to the treatment. Pharmacogenomics can also be used in pharmaceutical research to assist the drug development and selection process. Linder et al., Clinical Chemistry 43:254 (1997); Marshall, Nature Biotechnology 15:1249 (1997); International Patent Application WO 97/40462, Spectra Biomedical; and Schafer et al., Nature Biotechnology 16:3 (1998).